Abstract
If you follow the advice I have given in previous chapters, the chances are good that the results of your multiple imputation and analysis will be good. However, unforeseen things happen. Also, if you happen to be helping another person with these analyses, the material in this chapter will give some strategies for working through the problems.
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Notes
- 1.
But do recall that convergence is a function of the parameter values, and is not a direct function of the function value. Still, it is more common with “normal” EM convergence that the function value makes very small (monotonic) changes just prior to convergence.
- 2.
I also performed the same analysis with SPSS 19. Strategies and code for performing these analyses in SAS and SPSS are provided on our website, http://methodology.psu.edu.
- 3.
In SPSS, I found the principal components analysis to be as described above. However, I was somewhat less able to make use of the regression analysis to point further to the variable that should be removed, because SPSS does not have the option of including variables in the order presented. Thus, posatt1 was omitted from the analysis based on the F-to-enter criterion. On the other hand, the variable that was removed automatically (posatt1) did show that the problem was probably in that set of variables.
References
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Graham, J.W. (2012). Practical Issues Relating to Analysis with Missing Data: Avoiding and Troubleshooting Problems. In: Missing Data. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4018-5_8
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